Practical LLM Performance Evaluation

Integrating large language models (LLMs) into applications opens up new possibilities, but ensuring they perform reliably and efficiently is crucial for success. This course will equip you with the foundational knowledge and practical methods to systematically evaluate LLM outputs and apply optimization techniques, transforming your LLM-powered applications from experimental to robust. What you'll learn: * Understand core concepts and metrics for evaluating LLM performance. * Apply various evaluation strategies, including human-in-the-loop and automated methods. * Implement prompt engineering techniques to improve LLM output quality and efficiency. * Learn strategies for optimizing LLM cost and latency in real-world scenarios. * Analyze common failure modes and debug LLM responses effectively. * Configure basic retrieval-augmented generation (RAG) evaluation patterns. The course begins with fundamental definitions and moves through practical evaluation frameworks, common optimization strategies, and techniques to refine LLM behavior through iterative testing. This course is designed for beginners with no prior experience in LLM evaluation or optimization, who are looking to build reliable AI applications. Develop the essential skills to build and maintain high-performing LLM systems.